How difficult is it for small organizations to tune/build their own LLMs? Will every company eventually have its own little datacenter to train these LLMs? @husseinmuhaisen @GabiImmelman
COMPUTING
-
MIT Flexible Breadboard Accelerates Electronic Prototyping for Makers
By
–
Flexible MIT breadboard enables rapid prototyping w/interactive components, potentially streamlining the electronic design process for makers: http://
bit.ly/3qc7C8A -
Jensen Huang Keynote AI Innovations Computex 2023
By
–
For the first time in over 3 years, our CEO Jensen Huang will deliver a live keynote onstage at #COMPUTEX2023. Tune in for the keynote livestream to discover the latest #AI innovations and technologies. Sunday, May 28 at 8 p.m. PT. https://
nvda.ws/45u2j4l -
US Universities Building New Semiconductor Workforce
By
–
U.S. Universities Are Building a New Semiconductor Workforce https://
bit.ly/3WtUx6A -
Computer Vision and AI Collaboration: Major Organizations Drive Innovation
By
–
@jeevprabnivash @nuscenes @nuTonomy @Aptiv @Flickr @Wikipedia @Princeton @UniofOxford @Microsoft @_DigitalIndia @GoI_MeitY @nasscom @NeGD_GoI #computervision #ArtificialIntelligence #MachineLearning #INDIAai
-
Intelligent Automation and AI Transform Modern Workflows
By
–
Embracing the era of intelligent automation with AI! From robotic process automation to cognitive computing, read how AI technologies are transforming workflows, enhancing collaboration, and fuelling innovation: https://
rb.gy/sr9md -
MATLAB’s Impact on Engineering Innovation and Research
By
–
Discover @MATLAB
's remarkable influence on engineering innovation and research http://
spr.ly/6019OWSmP #Engineering #Innovation #STEM -
Blackrock’s 50 Brain Implants Challenge Neuralink’s Market
By
–
Looks like Neuralink are playing catch up. 50 humans have already got brain-computer implants via Blackrock. (via @PotentProzac
) -
Differentially-Private Algorithm for Hierarchical Graph Clustering
By
–
Introducing a new differentially-private algorithm for clustering hierarchical graphs plus open-source code for a scalable differentially-private k-means algorithm, which can be applied to large datasets using distributed computing. Learn more at:
-
Cerebras Model Lab: Compare LLMs, Estimate Training Costs
By
–
Introducing Model Lab: a new tool we built to make sense of training LLMs! The app lets you easily: – compare different models (LLaMA, Pythia etc)
– estimate training FLOPS, memory, inference cost
– visualize loss curves
Give it a try here https://
cerebras.net/model-lab/